Lakehouse Explained for 2025: What You Need to Know

·September 22, 2025

·13 min read

Lakehouse Explained for 2025: What You Need to Know — Image Source: pexels

A data lakehouse lets you keep all your data in one place. You can store both structured and unstructured data. You can run analytics quickly. This way, you do not have to move data between systems. Many groups say they save 50–75% on costs after using lakehouse solutions. Object storage can cost as little as $0.02 per GB.

Cost Savings	Description
50–75%	Groups usually save a lot of money after using lakehouse architectures.
50%	More than half of groups think they will save over 50% on analytics costs by using lakehouse architectures.
$0.02/GB	Object storage can cost only $0.02 per GB for the first 50TB each month.

With features like Iceberg, you get better ways to manage data and faster analytics.

Key Takeaways

A lakehouse puts all your data in one spot. This helps you find answers faster. It also makes managing data easier. You do not have to worry about data silos.
A lakehouse can help companies save a lot of money. It can cut costs by 50-75%. You do not need to buy costly hardware. You also do not need many different systems.
Lakehouses work with both structured and unstructured data. This makes it simple to do real-time analytics. It also helps with AI projects.
Good governance in lakehouses keeps data safe. It helps you follow rules. You can choose who can see or change the data.
Picking the right lakehouse for your business is important. It can make data management better. It also helps you get answers faster.

Fundamentals

Definition

A data lakehouse is one place for all your data. You can keep tables and also things like pictures or documents. You do not need to move your data to other systems. This makes your work quicker and easier.

A lakehouse uses one system for everything. You can do business tasks and analytics in the same place. You do not need different tools for storing and analyzing data. You get fast results and can handle many jobs.

Tip: With a lakehouse, you do not have data silos. You can manage all your data together. This helps you make smarter choices.

Core Components of a Lakehouse

Every lakehouse has some important parts:

Ingestion layer: This part brings data from many places into your lakehouse.
Storage layer: You keep all kinds of data here. It saves money and often uses object storage.
Metadata layer: This part tracks your data. It uses a catalog to help you find tables and files.
API layer: APIs help you and your tools know what data is there and how to use it.
Consumption layer: Here, you use business apps to get value from your data.

Think about these questions when you set up your lakehouse:

Where will you keep your data?
How will you track and control your tables?
What tools will you use to write data into the lakehouse?
How will you use Iceberg tables and other formats?
What tools will you use to study and show your data?

Here are some tools and engines you might use in a lakehouse:

Table Format Engine: Delta Lake, Iceberg, or Hudi
Query Engine: Trino, Athena, or BigQuery
Data Catalog: AWS Glue, Databricks Unity, or Collibra
Ingestion Tools: Apache Spark, Flink, or Kafka Connect
Consumption Tools: Tableau, Power BI, or dbt

How Lakehouse Differs from Traditional Systems

You may wonder how a lakehouse is different from older systems. The table below shows the main differences:

Feature	Lakehouse Architecture	Traditional Systems (OLTP/OLAP)
Integration of Layers	Deep connection of business and analytics layers	Separate business (OLTP) and analytics (OLAP) systems
Performance	Fast results	Often only batch processing
Modularity	Modular design, no silos	Usually one big system
Workload Support	Handles both business and analytics jobs	Only does business or analytics jobs
Data Management	Manages all data together	Hard to manage separate systems

A lakehouse helps you do more with less work. You can keep, manage, and study all your data in one place. This way saves time and money. It also gets you ready for new data and AI tools.

Benefits

Governance

You need good rules to keep your data safe. Lakehouse platforms help you control your data better than old systems. You can set who can see or change data. You can check changes and make sure only the right people see private information.

Lakehouses use metadata to keep data quality high. You can find problems and fix them faster.

Here are some common ways to manage data:

Governance Model	Description
Centralized Governance	Administrators control the metastore and set permissions for everything.
Distributed Governance	Owners of catalogs manage their own data rules.
DAMA-DMBOK	This framework connects data governance to other data practices.
DGI	This model focuses on who is responsible and how to measure data governance.
Atlan Active Governance	Automation makes it easier to manage data in modern systems.

Lakehouse platforms help you follow rules and laws about data. You can set controls to meet industry standards. You do not have to worry about data silos because you manage everything together.

Lakehouses make it easy to search data, which helps you manage it better.
You can use automation to check data quality and fix problems fast.
You can see who uses your data and what they do with it.

Cost Savings

Lakehouse technology helps you save money in many ways. You do not need to buy expensive hardware or pay for extra systems. You can use cheap storage and add more space or power only when you need it.

Operational Expense Type	Impact of Lakehouse Technology
Data Storage Costs	You pay less because you use low-cost storage.
Maintenance Costs	You save money by not running a separate warehouse.
Scalability Costs	You can grow your system without spending too much.

Moving to a lakehouse can cut costs by 77% to 95% compared to old warehouses. You do not need many copies of your data. You can add storage and computing power separately, so you only pay for what you use.

You lower your total cost by keeping all your data in one place.
You use cheap object storage, which lowers your bills.
You do not need to pay for extra tools or systems.

Many groups say they save more than half on analytics costs after switching to lakehouse architectures.

Analytics

Lakehouses help you get answers from your data faster. You can run reports and study information right away. You do not have to wait for data to move between systems.

Feature	Lakehouse	Legacy Systems
Data Integration	You connect many sources in one place.	You need to clean and model data first.
Access Speed	You get instant access for real-time analytics.	You wait for data to move.
Reporting Efficiency	You get insights quickly.	You get slower reports.
Flexibility in Data Sources	You use many formats and systems.	You use only a few types of data.
Adaptability Post-Acquisition	You combine data easily after mergers.	You struggle with split data.
User Accessibility	You use SQL to query data easily.	You need special skills to get data.

Lakehouse platforms make things run faster. For example, a travel company saw reports get 3.36 times quicker by using data caching. An online store ran queries faster after switching engines.

You get real-time answers, so you can decide quickly.
You can study both structured and unstructured data together.
You do not need special skills to run queries; you can use simple tools.

Lakehouses let you work with all your data at once. You get faster answers and better results.

Comparison

Data Lakes besides Warehouses

You might ask how data lakes and warehouses are different. Data lakes keep raw data in many formats. You can store structured, semi-structured, and unstructured data. Data warehouses only keep processed and structured data. Data lakes give you more choices. You do not need strict rules before adding data.

Feature	Data Warehouse	Data Lake
Data format	Processed, structured format	Raw, native format (all types)
Flexibility	Less flexible	Highly flexible
Setup effort	More time and work upfront	Easier setup, less effort
Data ingestion	ETL (Extract, Transform, Load)	ELT (Extract, Load, Transform)
Historical data	Processed, historical only	Raw data kept forever
User accessibility	Needs technical skills	Easy to extract, needs cleaning
Governance	Strong controls	Often weaker controls
Performance	Fast, complex queries	Variable, needs optimization

Data lakes can grow fast. You pay less for storage space. You can keep lots of data. Warehouses cost more and grow in steps. Warehouses run fast queries but are less flexible.

Data lakes help you try new ideas quickly. You do not need to plan everything first.

Data Warehouses on top of Data Lake

You can put a data warehouse on a data lake. This lets you use the lake for storage and the warehouse for studying data. You get both benefits. You keep all your data in the lake. You process and study it in the warehouse.

Data lakes have slower queries because they are not optimized.
Data warehouses run hard queries quickly.
Lakehouses add things like caching and indexing. You get faster queries than with just a data lake.

Lakehouses also make metadata management better. You get stronger rules and easier ETL steps. You can run regular queries and analytics. You keep flexibility and get more speed.

Lakehouses mix the good parts of lakes and warehouses. You get flexible storage and quick analytics.

Which types of Lakehouse is better?

Pick the lakehouse type that fits what you need. Think about your jobs, your team’s skills, and your tools.

Criteria	Description
Workload Characteristics	Know your main tasks and limits.
Existing Technology Stack	Look at what tools you already use.
Team Expertise	Check your team’s skills with data tools.
Scale Requirements	Decide how big your data system needs to be.
Update Patterns	See how often you update your data.

Think about your data needs and what you want to do.
Find out what kinds of data you have.
Decide what you want your lakehouse to do.

You get the best results when your lakehouse matches your business goals. Pick a setup that helps your data, your team, and your future plans.

Architecture

Storage Layer

Your lakehouse starts with a strong storage layer. This layer keeps all your data safe. You can find your data easily. You use layers to organize your data. There are three main layers: raw, curated, and final. Each layer helps make your data better. You can fix or rebuild data if you need.

Raw Layer (Bronze): You collect source data here. You can rebuild other layers from this base.
Curated Layer (Silver): You clean and refine data in this layer. It gives you a solid base for analysis.
Final Layer (Gold): You shape data for business needs. You get high-quality data for decision-making.

ACID transactions help keep your data safe. Managed services like Databricks help you grow and keep things working. Your storage layer works with other lakehouse parts. These include ingestion, metadata, processing engines, APIs, and governance.

Tip: Use layers to organize your data. This makes your lakehouse strong and easy to grow.

Metadata

Metadata helps you keep track of your data. It shows what data you have and how it is set up. Metadata also shows how your data changes over time. Good metadata makes your lakehouse faster and easier to use.

Role of Metadata	Description
Schema Management	You define the structure of datasets and keep them consistent.
Data Partitioning and Indexing	You store and access data quickly by using smart strategies.
Data Quality Enforcement	You set standards and check for problems in your data.
Workload Optimization	You make queries run faster by using resources wisely.
Version Control and Auditing	You keep old versions and follow rules for compliance.
Unified Analytics	You connect different types of data for easy analysis.

Modern metadata tools help you control your data. You get correct and easy-to-find data. You can use smart queries to get answers faster. You can connect many data sources. You can also make your data better for real-time analytics.

Iceberg

Iceberg makes your lakehouse more reliable. It lets you change your data setup without losing old data. You can look at older versions of your data. This helps you fix mistakes and follow rules.

Iceberg gives you reliable transactions. You know your operations finish completely or not at all.
You avoid problems like partial writes that can corrupt data lakes.
Iceberg protects against concurrency issues, so many users can work at once.
Key features include schema evolution, ACID guarantees, and time travel.

Iceberg fixes problems found in older data lakes. You get better data safety and version control. You can trust your lakehouse to keep your data safe.

Processing Engines

You need strong engines to study your data. Some popular engines are Dremio, Databricks Lakehouse, Starburst, and Snowflake. These engines help you get answers fast.

Optimization Technique	Description
OneLake Indexing	You create indexes to speed up data retrieval.
Materialized Views & Caching	You store query results for faster access.
Predicate Pushdown	You filter data early to process less information.
Broadcast Joins	You join tables efficiently by sharing small tables across nodes.
Vectorized Execution	You process many rows at once for better performance.
Bucketing	You spread data evenly for efficient joins.
Precomputed Aggregations	You use stored values to avoid recalculating during queries.
Auto-Scaling Compute	You adjust resources based on demand to save money.
Data Lifecycle Management	You keep hot data on fast storage and move cold data to cheaper options.
Compression and Deduplication	You reduce storage costs by shrinking large datasets.

These engines and tricks make your lakehouse fast and cheap. You get answers right away. You can handle lots of data easily.

Practical Considerations

Why Lakehouse is the foundation for AI+Data

You need a strong base for AI and data work. Lakehouse architecture gives you this base. You can bring together many kinds of data, like text, images, and numbers. You can use analytics and AI tools in one place. This helps you get more value from your data.

You can combine different data types. This makes it easier to use for AI and analytics.
You can support both regular and generative AI jobs.
You can trust your data because lakehouses keep data quality high and follow rules.
You can build on cloud object storage. This keeps raw data safe and easy to reach.
You can use ACID transactions and schema enforcement with Delta Lake and Iceberg. This keeps your data reliable.

Lakehouse platforms help you collect, organize, and connect trusted data. You can get the most value from your data for your group. You can also meet security and rule needs with strong controls.

Tip: When you use a lakehouse, you help your business do well with AI and smart choices.

Advantage	Description
Seamless integration of AI tools	You can add AI tools to your lakehouse easily. This helps you do more things.
Real-time analytics capabilities	You get answers right away. This is important for AI projects.
Robust data governance	You keep your data safe and follow rules. This is key for AI and following laws.
Support for traditional and generative AI	You can use old and new AI methods. This makes your data more useful.

Use Cases

You can use lakehouse architecture in many ways. Most groups now use lakehouses for building AI models. You can make an AI-ready data system. This helps you make better choices and work faster.

You can handle lots of streaming data for Internet of Things (IoT) jobs.
You can make money from your data by selling data services or market insights.
You can speed up new ideas and get ahead of others.
You can make your work better and spend less money.

Many industries use lakehouse solutions:

Industry	Scenario Description	Benefits of Lakehouse Solutions
Retail & E-Commerce	You can bring together data from sales, websites, and ads.	You get one place for all data types. This makes analytics and machine learning easier.
Manufacturing & IoT	You can use sensor data in real time for fixing machines before they break.	You can mix batch and streaming data in one system.
Finance	You can keep transaction data and follow rules.	You get one storage place with full analytics and rule checks.

You can see real results. For example, WeChat rebuilt its platform using an open lakehouse stack. They cut data engineering work in half and lowered storage costs by over 65%. They also made queries faster and made work steps simpler.

Note: You can use lakehouse solutions in healthcare, finance, and retail. Banks can spot fraud right away. Hospitals can mix different patient data types. Stores can quickly learn about customer trends.

Lakehouse architecture helps you save money and manage data in one place. You can study your data right away. You can also use it for smart AI projects. When you make a plan for your data, remember these important ideas:

Key Takeaway	Description
Lakehouse as a Transition	Use lakehouse to move to new data systems.
Align Expectations	Make sure your goals fit lakehouse benefits.
Business Justification	Try to spend less and grow easily.
Future-Oriented Design	Build for fast analytics and smart AI.

Lakehouses help you get ready for new tech. You can control your data better and reach your goals faster.

FAQ

What is the main advantage of a lakehouse?

You get one place for all your data. You can store, manage, and analyze everything together. This saves you time and money. You do not need to move data between systems.

Can you use lakehouse for AI projects?

Yes, you can use lakehouse for AI. You can combine different data types. You can run analytics and build AI models in the same system. This helps you work faster and smarter.

How does lakehouse help with data security?

Lakehouse platforms let you set rules for who can see or change data. You can track changes and control access. You keep your data safe and follow laws.

What tools work with lakehouse architecture?

You can use tools like Apache Spark, Trino, Tableau, and Power BI. These tools help you move, study, and show your data. You can pick the tools that fit your needs.

Tip: Try different tools to find what works best for your team.